### Abstract

Constraint satisfaction programming (CSP) has been successfully used in model-driven development (MDD) for solving a wide range of (combinatorial) problems. In CSP, declarative constraints capture restrictions over variables with finite domains where both the number of variables and their domains are required to be a priori finite. However, the existing formulation of constraint satisfaction problems can be too restrictive to support dynamically evolving domains and constraints necessitated in many MDD applications as the graph nature of the underlying models needs to be encoded with variables of finite domain. In the paper, we reformulate the constraint satisfaction problem directly on the model-level by using graph patterns as constraints and graph transformation rules as labeling operations. This allows expressing problems composed of dynamic model manipulation and complex graph structural constraints in an intuitive way. Furthermore, we present a prototype constraint solver for the domain of graph models built upon the Viatra2 model transformation framework, and provide an initial evaluation of its performance.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 107-121 |

Number of pages | 15 |

Volume | 5795 LNCS |

DOIs | |

Publication status | Published - 2009 |

Event | 12th International Conference on Model Driven Engineering Languages and Systems, MODELS 2009 - Denver, CO, United States Duration: Oct 4 2009 → Oct 9 2009 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 5795 LNCS |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 12th International Conference on Model Driven Engineering Languages and Systems, MODELS 2009 |
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Country | United States |

City | Denver, CO |

Period | 10/4/09 → 10/9/09 |

### Fingerprint

### Keywords

- Constraint satisfaction programming
- Graph transformation

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 5795 LNCS, pp. 107-121). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5795 LNCS). https://doi.org/10.1007/978-3-642-04425-0_9

**CSP(M) : Constraint satisfaction problem over models.** / Horváth, Ákos; Varró, D.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 5795 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5795 LNCS, pp. 107-121, 12th International Conference on Model Driven Engineering Languages and Systems, MODELS 2009, Denver, CO, United States, 10/4/09. https://doi.org/10.1007/978-3-642-04425-0_9

}

TY - GEN

T1 - CSP(M)

T2 - Constraint satisfaction problem over models

AU - Horváth, Ákos

AU - Varró, D.

PY - 2009

Y1 - 2009

N2 - Constraint satisfaction programming (CSP) has been successfully used in model-driven development (MDD) for solving a wide range of (combinatorial) problems. In CSP, declarative constraints capture restrictions over variables with finite domains where both the number of variables and their domains are required to be a priori finite. However, the existing formulation of constraint satisfaction problems can be too restrictive to support dynamically evolving domains and constraints necessitated in many MDD applications as the graph nature of the underlying models needs to be encoded with variables of finite domain. In the paper, we reformulate the constraint satisfaction problem directly on the model-level by using graph patterns as constraints and graph transformation rules as labeling operations. This allows expressing problems composed of dynamic model manipulation and complex graph structural constraints in an intuitive way. Furthermore, we present a prototype constraint solver for the domain of graph models built upon the Viatra2 model transformation framework, and provide an initial evaluation of its performance.

AB - Constraint satisfaction programming (CSP) has been successfully used in model-driven development (MDD) for solving a wide range of (combinatorial) problems. In CSP, declarative constraints capture restrictions over variables with finite domains where both the number of variables and their domains are required to be a priori finite. However, the existing formulation of constraint satisfaction problems can be too restrictive to support dynamically evolving domains and constraints necessitated in many MDD applications as the graph nature of the underlying models needs to be encoded with variables of finite domain. In the paper, we reformulate the constraint satisfaction problem directly on the model-level by using graph patterns as constraints and graph transformation rules as labeling operations. This allows expressing problems composed of dynamic model manipulation and complex graph structural constraints in an intuitive way. Furthermore, we present a prototype constraint solver for the domain of graph models built upon the Viatra2 model transformation framework, and provide an initial evaluation of its performance.

KW - Constraint satisfaction programming

KW - Graph transformation

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U2 - 10.1007/978-3-642-04425-0_9

DO - 10.1007/978-3-642-04425-0_9

M3 - Conference contribution

SN - 3642044247

SN - 9783642044243

VL - 5795 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 107

EP - 121

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -